The quest for simple solutions is not new in machine learning (ML) and related methods such as genetic programming (GP). GP is a nature-inspired approach to the automatic programming of computers used to create solutions to a broad range of computational problems. However, the evolving solutions can grow unnecessarily complex, which presents considerable challenges. Typically, the control of complexity in GP means reducing the sizes of the evolved expressions – known as bloat-control. However, size is a function of solution representation, and hence it does not consistently capture complexity across diverse GP applications. Instead, this thesis proposes to estimate the complexity of the evolving solutions by their evaluation time – the comp...
In classification,machine learning algorithms can suffer a performance bias when data sets are unbal...
In classification,machine learning algorithms can suffer a performance bias when data sets are unbal...
The last decade has seen amazing performance improvements in deep learning. However, the black-box n...
The quest for simple solutions is not new in machine learning (ML) and related methods such as genet...
Traditionally, reducing complexity in Machine Learning promises benefits such as less overfitting. H...
In genetic programming (GP), controlling complexity often means reducing the size of evolved express...
Complexity of evolving models in genetic programming (GP) can impact both the quality of the models ...
In machine learning, reducing the complexity of a model can help to improve its computational effici...
In machine learning, reducing the complexity of a model can help to improve its computational effici...
Genetic programming (GP), a widely used evolutionary computing technique, suffers from bloat—the pro...
Genetic Programming is an evolutionary computation technique which searches for those computer progr...
One of the greater issues in Genetic Programming (GP) is the computational effort required to run th...
© The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 Internationa...
Feature construction can substantially improve the accuracy of Machine Learning (ML) algorithms. Gen...
Genetic programming (GP) is an evolutionary computation technique to solve problems in an automated,...
In classification,machine learning algorithms can suffer a performance bias when data sets are unbal...
In classification,machine learning algorithms can suffer a performance bias when data sets are unbal...
The last decade has seen amazing performance improvements in deep learning. However, the black-box n...
The quest for simple solutions is not new in machine learning (ML) and related methods such as genet...
Traditionally, reducing complexity in Machine Learning promises benefits such as less overfitting. H...
In genetic programming (GP), controlling complexity often means reducing the size of evolved express...
Complexity of evolving models in genetic programming (GP) can impact both the quality of the models ...
In machine learning, reducing the complexity of a model can help to improve its computational effici...
In machine learning, reducing the complexity of a model can help to improve its computational effici...
Genetic programming (GP), a widely used evolutionary computing technique, suffers from bloat—the pro...
Genetic Programming is an evolutionary computation technique which searches for those computer progr...
One of the greater issues in Genetic Programming (GP) is the computational effort required to run th...
© The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 Internationa...
Feature construction can substantially improve the accuracy of Machine Learning (ML) algorithms. Gen...
Genetic programming (GP) is an evolutionary computation technique to solve problems in an automated,...
In classification,machine learning algorithms can suffer a performance bias when data sets are unbal...
In classification,machine learning algorithms can suffer a performance bias when data sets are unbal...
The last decade has seen amazing performance improvements in deep learning. However, the black-box n...